Impact of cumulative body mass index and cardiometabolic diseases on survival among patients with colorectal and breast cancer: a multi-centre cohort study

BACKGROUND: Body mass index (BMI) and cardiometabolic comorbidities such as cardiovascular disease and type 2 diabetes have been studied as negative prognostic factors in cancer survival, but possible dependencies in the mechanisms underlying these associations remain largely unexplored. We analysed these associations in colorectal and breast cancer patients. METHODS: Based on repeated BMI assessments of cancer-free participants from four European countries in the European Prospective Investigation into Cancer and nutrition (EPIC) study, individual BMI-trajectories reflecting predicted mean BMI between ages 20 to 50 years were estimated using a growth curve model. Participants with incident colorectal or breast cancer after the age of 50 years were included in the survival analysis to study the prognostic effect of mean BMI and cardiometabolic diseases (CMD) prior to cancer. CMD were defined as one or more chronic conditions among stroke, myocardial infarction, and type 2 diabetes. Hazard ratios (HRs) and confidence intervals (CIs) of mean BMI and CMD were derived using multivariable-adjusted Cox proportional hazard regression for mean BMI and CMD separately and both exposures combined, in subgroups of localised and advanced disease. RESULTS: In the total cohort of 159,045 participants, there were 1,045 and 1,620 eligible patients of colorectal and breast cancer. In colorectal cancer patients, a higher BMI (by 1 kg/m2) was associated with a 6% increase in risk of death (95% CI of HR: 1.02-1.10). The HR for CMD was 1.25 (95% CI: 0.97-1.61). The associations for both exposures were stronger in patients with localised colorectal cancer. In breast cancer patients, a higher BMI was associated with a 4% increase in risk of death (95% CI: 1.00-1.08). CMDs were associated with a 46% increase in risk of death (95% CI: 1.01-2.09). The estimates and CIs for BMI remained similar after adjustment for CMD and vice versa. CONCLUSIONS: Our results suggest that cumulative exposure to higher BMI during early to mid-adulthood was associated with poorer survival in patients with breast and colorectal cancer, independent of CMD prior to cancer diagnosis. The association between a CMD diagnosis prior to cancer and survival in patients with breast and colorectal cancer was independent of BMI

Metabolic Mediators of the Association Between Adult Weight Gain and Colorectal Cancer: Data From the European Prospective Investigation Into Cancer and Nutrition (EPIC) Cohort

Evidence indicates that gaining weight in adult life is associated with an elevated risk of colorectal cancer; however, biological mechanisms that may explain this association remain unclear. We evaluated the mediation effect of 20 different biomarkers on the relationship between adult weight gain and colorectal cancer, using data from a prospective nested case-control study of 452 incident cases diagnosed between 1992 and 2003 and matched within risk sets to 452 controls within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. The proportions of mediated effects (%) were estimated on the basis of differences in percent effect changes in conditional logistic regression models with and without additional adjustment for individual biomarkers. Greater adult weight gain (≥300 g/year vs. <300 g/year) was associated with a higher risk of colon cancer (multivariable-adjusted relative risk = 1.54, 95% confidence interval: 1.07, 2.24) but not rectal cancer (relative risk = 1.07, 95% confidence interval: 0.68, 1.66). This association was accounted for mostly by attained waist circumference (reduction of 61%) and by the biomarkers soluble leptin receptor (reduction of 43%) and glycated hemoglobin (reduction of 28%). These novel data suggest that the observed association between adult weight gain and colon cancer could be primarily explained by attained abdominal fatness and biomarkers of metabolic dysfunction

Prospective evaluation of 92 serum protein biomarkers for early detection of ovarian cancer

Background CA125 is the best available yet insufficiently sensitive biomarker for early detection of ovarian cancer. There is a need to identify novel biomarkers, which individually or in combination with CA125 can achieve adequate sensitivity and specificity for the detection of earlier-stage ovarian cancer. Methods In the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort, we measured serum levels of 92 preselected proteins for 91 women who had blood sampled ≤18 months prior to ovarian cancer diagnosis, and 182 matched controls. We evaluated the discriminatory performance of the proteins as potential early diagnostic biomarkers of ovarian cancer. Results Nine of the 92 markers; CA125, HE4, FOLR1, KLK11, WISP1, MDK, CXCL13, MSLN and ADAM8 showed an area under the ROC curve (AUC) of ≥0.70 for discriminating between women diagnosed with ovarian cancer and women who remained cancer-free. All, except ADAM8, had shown at least equal discrimination in previous case-control comparisons. The discrimination of the biomarkers, however, was low for the lag-time of >9–18 months and paired combinations of CA125 with any of the 8 markers did not improve discrimination compared to CA125 alone. Conclusion Using pre-diagnostic serum samples, this study identified markers with good discrimination for the lag-time of 0–9 months. However, the discrimination was low in blood samples collected more than 9 months prior to diagnosis, and none of the markers showed major improvement in discrimination when added to CA125

Diet-wide association study of 92 foods and nutrients and lung cancer risk in the European Prospective Investigation into Cancer and Nutrition study and the Netherlands Cohort Study

Diet-wide association study of 92 foods and nutrients and lung cancer risk in the European Prospective Investigation into Cancer and Nutrition study and the Netherlands Cohort Stud

Energy and macronutrient intake and risk of differentiated thyroid carcinoma in the European Prospective Investigation into Cancer and Nutrition study.

Incidence rates of differentiated thyroid carcinoma (TC) have increased in many countries. Adiposity and dietary risk factors may play a role, but little is known on the influence of energy intake and macronutrient composition. The aim of this study was to investigate the associations between TC and the intake of energy, macronutrients, glycemic index (GI) and glycemic load in the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. The study included 477,274 middle-age participants (70.2% women) from ten European countries. Dietary data were collected using country-specific validated dietary questionnaires. Total carbohydrates, proteins, fats, saturated, monounsaturated and polyunsaturated fats (PUFA), starch, sugar, and fiber were computed as g/1,000 kcal. Multivariable Cox regression was used to calculate multivariable adjusted hazard ratios (HR) and 95% confidence interval (CI) by intake quartile (Q). After a mean follow-up time of 11 years, differentiated TC was diagnosed in 556 participants (90% women). Overall, we found significant associations only with total energy (HRQ4 vs .Q1 , 1.29; 95% CI, 1.00-1.68) and PUFA intakes (HRQ4 vs .Q1 , 0.74; 95% CI, 0.57-0.95). However, the associations with starch and sugar intake and GI were significantly heterogeneous across body mass index (BMI) groups, i.e., positive associations with starch and GI were found in participants with a BMI ≥ 25 and with sugar intake in those with BMI &lt; 25. Moreover, inverse associations with starch and GI were observed in subjects with BMI &lt; 25. In conclusion, our results suggest that high total energy and low PUFA intakes may increase the risk of differentiated TC. Positive associations with starch intake and GI in participants with BMI ≥ 25 suggest that those persons may have a greater insulin response to high starch intake and GI than lean people

Meal patterns across 10 European countries – results from the European Prospective Investigation into Cancer and Nutrition (EPIC) calibration study

Objective To characterize meal patterns across ten European countries participating in the European Prospective Investigation into Cancer and Nutrition (EPIC) calibration study. Design Cross-sectional study utilizing dietary data collected through a standardised 24-h diet recall during 1995-2000. Eleven predefined intake occasions across a 24-h period were assessed during the interview. In this descriptive report, meal patterns were analysed in terms of daily number of intake occasions, the proportion reporting each intake occasion and the energy contributions from each intake occasion. Setting Twenty-seven centres across ten European countries. Subjects 36020 women (64%) and men (36%) aged 35-74 years. Results Pronounced differences in meal patterns emerged both across centres within the same country and across different countries with a trend for fewer intake occasions/day in countries as compared to central and northern Europe. Differences were also found for daily energy intake provided by lunch, with 38-43% for women and 41-45% for men within Mediterranean countries compared to 16-27% for women and 20-26% for men in central and northern European countries. Likewise, a south-north gradient was found for daily energy intake from snacks, with 13-20% (women) and 10-17% (men) in Mediterranean countries compared to 24-34% (women) and 23-35% (men) in central/northern Europe. Conclusion We found distinct differences in meal patterns with marked diversity for intake frequency and lunch and snack consumption between Mediterranean and central/northern European countries. Monitoring of meal patterns across various cultures and populations could provide critical context to the research efforts to characterize relationships between dietary intake and health.</p

Circulating metabolites associated with alcohol intake in the European Prospective Investigation into Cancer and Nutrition cohort

Identifying the metabolites associated with alcohol consumption may provide insights into the metabolic pathways through which alcohol may affect human health. We studied associations of alcohol consumption with circulating concentrations of 123 metabolites among 2974 healthy participants from the European Prospective Investigation into Cancer and Nutrition (EPIC) study. Alcohol consumption at recruitment was self-reported through dietary questionnaires. Metabolite concentrations were measured by tandem mass spectrometry (BIOCRATES AbsoluteIDQTM p180 kit). Data were randomly divided into discovery (2/3) and replication (1/3) sets. Multivariable linear regression models were used to evaluate confounder-adjusted associations of alcohol consumption with metabolite concentrations. Metabolites significantly related to alcohol intake in the discovery set (FDR q-value &lt; 0.05) were further tested in the replication set (Bonferroni-corrected p-value &lt; 0.05). Of the 72 metabolites significantly related to alcohol intake in the discovery set, 34 were also significant in the replication analysis, including three acylcarnitines, the amino acid citrulline, four lysophosphatidylcholines, 13 diacylphosphatidylcholines, seven acyl-alkylphosphatidylcholines, and six sphingomyelins. Our results confirmed earlier findings that alcohol consumption was associated with several lipid metabolites, and possibly also with specific acylcarnitines and amino acids. This provides further leads for future research studies aiming at elucidating the mechanisms underlying the effects of alcohol in relation to morbid conditions

A new pipeline for the normalization and pooling of metabolomics data

Pooling metabolomics data across studies is often desirable to increase the statistical power of the analysis. However, this can raise methodological challenges as several preanalytical and analytical factors could introduce differences in measured concentrations and variability between datasets. Specifically, different studies may use variable sample types (e.g., serum versus plasma) collected, treated, and stored according to different protocols, and assayed in different laboratories using different instruments. To address these issues, a new pipeline was developed to normalize and pool metabolomics data through a set of sequential steps: (i) exclusions of the least informative observations and metabolites and removal of outliers; imputation of missing data; (ii) identification of the main sources of variability through principal component partial R-square (PC-PR2) analysis; (iii) application of linear mixed models to remove unwanted variability, including samples’ originating study and batch, and preserve biological variations while accounting for potential differences in the residual variances across studies. This pipeline was applied to targeted metabolomics data acquired using Biocrates AbsoluteIDQ kits in eight case-control studies nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. Comprehensive examination of metabolomics measurements indicated that the pipeline improved the comparability of data across the studies. Our pipeline can be adapted to normalize other molecular data, including biomarkers as well as proteomics data, and could be used for pooling molecular datasets, for example in international consortia, to limit biases introduced by inter-study variability. This versatility of the pipeline makes our work of potential interest to molecular epidemiologists

Circulating Metabolites Associated with Alcohol Intake in the European Prospective Investigation into Cancer and Nutrition Cohort

Identifying the metabolites associated with alcohol consumption may provide insights into the metabolic pathways through which alcohol may affect human health. We studied associations of alcohol consumption with circulating concentrations of 123 metabolites among 2974 healthy participants from the European Prospective Investigation into Cancer and Nutrition (EPIC) study. Alcohol consumption at recruitment was self-reported through dietary questionnaires. Metabolite concentrations were measured by tandem mass spectrometry (BIOCRATES AbsoluteIDQTM p180 kit). Data were randomly divided into discovery (2/3) and replication (1/3) sets. Multivariable linear regression models were used to evaluate confounder-adjusted associations of alcohol consumption with metabolite concentrations. Metabolites significantly related to alcohol intake in the discovery set (FDR q-value < 0.05) were further tested in the replication set (Bonferroni-corrected p-value < 0.05). Of the 72 metabolites significantly related to alcohol intake in the discovery set, 34 were also significant in the replication analysis, including three acylcarnitines, the amino acid citrulline, four lysophosphatidylcholines, 13 diacylphosphatidylcholines, seven acyl-alkylphosphatidylcholines, and six sphingomyelins. Our results confirmed earlier findings that alcohol consumption was associated with several lipid metabolites, and possibly also with specific acylcarnitines and amino acids. This provides further leads for future research studies aiming at elucidating the mechanisms underlying the effects of alcohol in relation to morbid conditions